Machine Learning System, Method, and Program Product for Point of Sale Systems

ABSTRACT

A machine learning innovation for point of sale systems is provided which includes a scanner component, which can scan at least one of the following codes: Barcode, QR code, RFID or any other new code and id, a camera component, which can get image or picture of objects, and a compute component with prediction algorithm to classify the object. The system also includes a method with prediction and learning capability that sends the classified labels to central controller or server. A central controller or server gathers classified labels and analyze and learn from classified labels information, and sends updated a scanner component, which can scan at least one of the codes.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present Utility patent application claims priority benefit of theU.S. provisional application for patent Ser. No. 62/156,848 entitled“MACHINE LEARNING SYSTEM ON RETAIL SHOPPING” filed 4 May 2015 under 35U.S.C. 119(e). The contents of this related provisional application areincorporated herein by reference for all purposes to the extent thatsuch subject matter is not inconsistent herewith or limiting hereof.

RELATED CO-PENDING U.S. PATENT APPLICATIONS

Not applicable.

FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

REFERENCE TO SEQUENCE LISTING, A TABLE, OR A COMPUTER LISTING APPENDIX

Not applicable.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection by the author thereof. Thecopyright owner has no objection to the facsimile reproduction by anyoneof the patent document or patent disclosure for the purposes ofreferencing as patent prior art, as it appears in the Patent andTrademark Office, patent file or records, but otherwise reserves allcopyright rights whatsoever.

BACKGROUND OF THE RELEVANT PRIOR ART

One or more embodiments of the invention generally relate to machinelearning systems. More particularly, certain embodiments of theinvention relates to machine learning systems in point of sale systems.

The following background information may present examples of specificaspects of the prior art (e.g., without limitation, approaches, facts,or common wisdom) that, while expected to be helpful to further educatethe reader as to additional aspects of the prior art, is not to beconstrued as limiting the present invention, or any embodiments thereof,to anything stated or implied therein or inferred thereupon.

The following is an example of a specific aspect in the prior art that,while expected to be helpful to further educate the reader as toadditional aspects of the prior art, is not to be construed as limitingthe present invention, or any embodiments thereof, to anything stated orimplied therein or inferred thereupon. In a traditional way, retailstores may either label each item with barcode or quick response (QR)code. Sometimes, they may need cashiers to remember an ID of each itemor non-labeled items. The ID could be a few letters, digits orcombination of letters and digits. The barcode, QR code or ID may beused for connecting to a database to get the price of that item. Apoint-of-sale (POS) is a place where a retail transaction may becompleted. It is the place that a customer makes a payment to themerchant in exchange for goods. At the point of sale, the retailer maycalculate the total amount customer needs to pay and provide options forthe customer to make a payment. The merchant may also normally issue areceipt for the transaction. There are various POS systems for differentretail industries uses. Different retail industries may use theircustomized hardware and software according to their requirements. Manyretailers may use weighing scales, scanners, electronic and manual cashregisters, terminals, touch screens and any other wide variety ofhardware and software available for us with POS. For example, a grocerystore may use a scale at the point of sale, while restaurants may usesoftware to customize service sold when a customer requests for a mealor drink. The point of sale may also be referred to as a point ofservice. It is because it is not just a point of sale, but also a pointof return or customer order. The POS system has many features such asinventory management, CRM, financials and warehousing, etc.

By way of educational background, another aspect of the prior artgenerally useful to be aware of is a cloud-based POS is a systemdeployed as software as a service, which may be accessed directly fromthe Internet by using an internet browser. Cloud-based POS systems maybe independent from platform and operating system limitations. It mayalso be designed to be compatible with a wide range of POS hardware andsometimes compatible with mobile devices. Cloud-based POS systems maystore data, and inventory in a remote server. The cloud-based POS systemmay not run locally, so there may be no installation required in thelocal store.

In view of the foregoing, it is clear that these traditional techniquesare not perfect and leave room for more optimal approaches.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings and in whichlike reference numerals refer to similar elements and in which:

FIG. 1 illustrates an exemplary flow diagram for machine learning, inaccordance with an embodiment of the present invention;

FIG. 2 illustrates an exemplary flow diagram for machine learning withkey entry, in accordance with an embodiment of the present invention;

FIG. 3 illustrates an exemplary prediction, in accordance with anembodiment of the present invention;

FIG. 4 illustrates an exemplary neural network architecture, inaccordance with an embodiment of the present invention;

FIG. 5 illustrates an exemplary integrated machine learning system, inaccordance with an embodiment of the present invention;

FIG. 6 illustrates an exemplary device with scanner and cameracapability, in accordance with an embodiment of the present invention;

FIG. 7 illustrates an exemplary block diagram of a device with scannerand camera capability, in accordance with an embodiment of the presentinvention;

FIG. 8 illustrates an exemplary block diagram of a surveillance system,in the prior art;

FIG. 9 illustrates an exemplary block diagram of a surveillance system,in accordance with an embodiment of the present invention;

FIG. 10 illustrates an exemplary block diagram of a surveillance system,in accordance with an embodiment of the present invention;

FIG. 11 illustrates an exemplary method for a search, in accordance withan embodiment of the present invention; and

FIG. 12 is a block diagram depicting an exemplary client/server systemwhich may be used by an exemplary web-enabled/networked embodiment ofthe present invention.

Unless otherwise indicated illustrations in the figures are notnecessarily drawn to scale.

DETAILED DESCRIPTION OF SOME EMBODIMENTS

The present invention is best understood by reference to the detailedfigures and description set forth herein.

Embodiments of the invention are discussed below with reference to theFigures. However, those skilled in the art will readily appreciate thatthe detailed description given herein with respect to these figures isfor explanatory purposes as the invention extends beyond these limitedembodiments. For example, it should be appreciated that those skilled inthe art will, in light of the teachings of the present invention,recognize a multiplicity of alternate and suitable approaches, dependingupon the needs of the particular application, to implement thefunctionality of any given detail described herein, beyond theparticular implementation choices in the following embodiments describedand shown. That is, there are modifications and variations of theinvention that are too numerous to be listed but that all fit within thescope of the invention. Also, singular words should be read as pluraland vice versa and masculine as feminine and vice versa, whereappropriate, and alternative embodiments do not necessarily imply thatthe two are mutually exclusive.

It is to be further understood that the present invention is not limitedto the particular methodology, compounds, materials, manufacturingtechniques, uses, and applications, described herein, as these may vary.It is also to be understood that the terminology used herein is used forthe purpose of describing particular embodiments only, and is notintended to limit the scope of the present invention. It must be notedthat as used herein and in the appended claims, the singular forms “a,”“an,” and “the” include the plural reference unless the context clearlydictates otherwise. Thus, for example, a reference to “an element” is areference to one or more elements and includes equivalents thereof knownto those skilled in the art. Similarly, for another example, a referenceto “a step” or “a means” is a reference to one or more steps or meansand may include sub-steps and subservient means. All conjunctions usedare to be understood in the most inclusive sense possible. Thus, theword “or” should be understood as having the definition of a logical“or” rather than that of a logical “exclusive or” unless the contextclearly necessitates otherwise. Structures described herein are to beunderstood also to refer to functional equivalents of such structures.Language that may be construed to express approximation should be sounderstood unless the context clearly dictates otherwise.

All words of approximation as used in the present disclosure and claimsshould be construed to mean “approximate,” rather than “perfect,” andmay accordingly be employed as a meaningful modifier to any other word,specified parameter, quantity, quality, or concept. Words ofapproximation, include, yet are not limited to terms such as“substantial”, “nearly”, “almost”, “about”, “generally”, “largely”,“essentially”, “closely approximate”, etc.

As will be established in some detail below, it is well settle law, asearly as 1939, that words of approximation are not indefinite in theclaims even when such limits are not defined or specified in thespecification.

For example, see Ex parte Mallory, 52 USPQ 297, 297 (Pat. Off. Bd. App.1941) where the court said “The examiner has held that most of theclaims are inaccurate because apparently the laminar film will not beentirely eliminated. The claims specify that the film is “substantially”eliminated and for the intended purpose, it is believed that the slightportion of the film which may remain is negligible. We are of the view,therefore, that the claims may be regarded as sufficiently accurate.”

Note that claims need only “reasonably apprise those skilled in the art”as to their scope to satisfy the definiteness requirement. See EnergyAbsorption Sys., Inc. v. Roadway Safety Servs., Inc., Civ. App. 96-1264,slip op. at 10 (Fed. Cir. Jul. 3, 1997) (unpublished) Hybridtech v.Monoclonal Antibodies, Inc., 802 F.2d 1367, 1385, 231 USPQ 81, 94 (Fed.Cir. 1986), cert. denied, 480 U.S. 947 (1987). In addition, the use ofmodifiers in the claim, like “generally” and “substantial,” does not byitself render the claims indefinite. See Seattle Box Co. v. IndustrialCrating & Packing, Inc., 731 F.2d 818, 828-29, 221 USPQ 568, 575-76(Fed. Cir. 1984).

Moreover, the ordinary and customary meaning of terms like“substantially” includes “reasonably close to: nearly, almost, about”,connoting a term of approximation. See In re Frye, Appeal No.2009-006013, 94 USPQ2d 1072, 1077, 2010 WL 889747 (B.P.A.I. 2010)Depending on its usage, the word “substantially” can denote eitherlanguage of approximation or language of magnitude. Deering PrecisionInstruments, L.L.C. v. Vector Distribution Sys., Inc., 347 F.3d 1314,1323 (Fed. Cir. 2003) (recognizing the “dual ordinary meaning of th[e]term [“substantially”] as connoting a term of approximation or a term ofmagnitude”). Here, when referring to the “substantially halfway”limitation, the Specification uses the word “approximately” as asubstitute for the word “substantially” (Fact 4). (Fact 4). The ordinarymeaning of “substantially halfway” is thus reasonably close to or nearlyat the midpoint between the forwardmost point of the upper or outsoleand the rearwardmost point of the upper or outsole.

Similarly, the term ‘substantially’ is well recognize in case law tohave the dual ordinary meaning of connoting a term of approximation or aterm of magnitude. See Dana Corp. v. American Axle & Manufacturing,Inc., Civ. App. 04-1116, 2004 U.S. App. LEXIS 18265, *13-14 (Fed. Cir.Aug. 27, 2004) (unpublished). The term “substantially” is commonly usedby claim drafters to indicate approximation. See Cordis Corp. v.Medtronic AVE Inc., 339 F.3d 1352, 1360 (Fed. Cir. 2003) (“The patentsdo not set out any numerical standard by which to determine whether thethickness of the wall surface is ‘substantially uniform.’ The term‘substantially,’ as used in this context, denotes approximation. Thus,the walls must be of largely or approximately uniform thickness.”); seealso Deering Precision Instruments, LLC v. Vector Distribution Sys.,Inc., 347 F.3d 1314, 1322 (Fed. Cir. 2003); Epcon Gas Sys., Inc. v.Bauer Compressors, Inc., 279 F.3d 1022, 1031 (Fed. Cir. 2002). We findthat the term “substantially” was used in just such a manner in theclaims of the patents-in-suit: “substantially uniform wall thickness”denotes a wall thickness with approximate uniformity.

It should also be noted that such words of approximation as contemplatedin the foregoing clearly limits the scope of claims such as saying‘generally parallel’ such that the adverb ‘generally’ does not broadenthe meaning of parallel. Accordingly, it is well settled that such wordsof approximation as contemplated in the foregoing (e.g., like the phrase‘generally parallel’) envisions some amount of deviation from perfection(e.g., not exactly parallel), and that such words of approximation ascontemplated in the foregoing are descriptive terms commonly used inpatent claims to avoid a strict numerical boundary to the specifiedparameter. To the extent that the plain language of the claims relyingon such words of approximation as contemplated in the foregoing areclear and uncontradicted by anything in the written description hereinor the figures thereof, it is improper to rely upon the present writtendescription, the figures, or the prosecution history to add limitationsto any of the claim of the present invention with respect to such wordsof approximation as contemplated in the foregoing. That is, under suchcircumstances, relying on the written description and prosecutionhistory to reject the ordinary and customary meanings of the wordsthemselves is impermissible. See, for example, Liquid Dynamics Corp. v.Vaughan Co., 355 F.3d 1361, 69 USPQ2d 1595, 1600-01 (Fed. Cir. 2004).The plain language of phrase 2 requires a “substantial helical flow.”The term “substantial” is a meaningful modifier implying “approximate,”rather than “perfect.” In Cordis Corp. v. Medtronic AVE, Inc., 339 F.3d1352, 1361 (Fed. Cir. 2003), the district court imposed a precisenumeric constraint on the term “substantially uniform thickness.” Wenoted that the proper interpretation of this term was “of largely orapproximately uniform thickness” unless something in the prosecutionhistory imposed the “clear and unmistakable disclaimer” needed fornarrowing beyond this simple-language interpretation. Id. In Anchor WallSystems v. Rockwood Retaining Walls, Inc., 340 F.3d 1298, 1311 (Fed.Cir. 2003)” Id. at 1311. Similarly, the plain language of claim 1requires neither a perfectly helical flow nor a flow that returnsprecisely to the center after one rotation (a limitation that arisesonly as a logical consequence of requiring a perfectly helical flow).

The reader should appreciate that case law generally recognizes a dualordinary meaning of such words of approximation, as contemplated in theforegoing, as connoting a term of approximation or a term of magnitude;e.g., see Deering Precision Instruments, L.L.C. v. Vector Distrib. Sys.,Inc., 347 F.3d 1314, 68 USPQ2d 1716, 1721 (Fed. Cir. 2003), cert.denied, 124 S. Ct. 1426 (2004) where the court was asked to construe themeaning of the term “substantially” in a patent claim. Also see Epcon,279 F.3d at 1031 (“The phrase ‘substantially constant’ denotes languageof approximation, while the phrase ‘substantially below’ signifieslanguage of magnitude, i.e., not insubstantial.”). Also, see, e.g.,Epcon Gas Sys., Inc. v. Bauer Compressors, Inc., 279 F.3d 1022 (Fed.Cir. 2002) (construing the terms “substantially constant” and“substantially below”); Zodiac Pool Care, Inc. v. Hoffinger Indus.,Inc., 206 F.3d 1408 (Fed. Cir. 2000) (construing the term “substantiallyinward”); York Prods., Inc. v. Cent. Tractor Farm & Family Ctr., 99 F.3d1568 (Fed. Cir. 1996) (construing the term “substantially the entireheight thereof”); Tex. Instruments Inc. v. Cypress Semiconductor Corp.,90 F.3d 1558 (Fed. Cir. 1996) (construing the term “substantially in thecommon plane”). In conducting their analysis, the court instructed tobegin with the ordinary meaning of the claim terms to one of ordinaryskill in the art. Prima Tek, 318 F.3d at 1148. Reference to dictionariesand our cases indicates that the term “substantially” has numerousordinary meanings. As the district court stated, “substantially” canmean “significantly” or “considerably.” The term “substantially” canalso mean “largely” or “essentially.” Webster's New 20th CenturyDictionary 1817 (1983).

Words of approximation, as contemplated in the foregoing, may also beused in phrases establishing approximate ranges or limits, where the endpoints are inclusive and approximate, not perfect; e.g., see AK SteelCorp. v. Sollac, 344 F.3d 1234, 68 USPQ2d 1280, 1285 (Fed. Cir. 2003)where it where the court said [W]e conclude that the ordinary meaning ofthe phrase “up to about 10%” includes the “about 10%” endpoint. Aspointed out by AK Steel, when an object of the preposition “up to” isnonnumeric, the most natural meaning is to exclude the object (e.g.,painting the wall up to the door). On the other hand, as pointed out bySollac, when the object is a numerical limit, the normal meaning is toinclude that upper numerical limit (e.g., counting up to ten, seatingcapacity for up to seven passengers). Because we have here a numericallimit—“about 10%”—the ordinary meaning is that that endpoint isincluded.

In the present specification and claims, a goal of employment of suchwords of approximation, as contemplated in the foregoing, is to avoid astrict numerical boundary to the modified specified parameter, assanctioned by Pall Corp. v. Micron Separations, Inc., 66 F.3d 1211,1217, 36 USPQ2d 1225, 1229 (Fed. Cir. 1995) where it states “It is wellestablished that when the term “substantially” serves reasonably todescribe the subject matter so that its scope would be understood bypersons in the field of the invention, and to distinguish the claimedsubject matter from the prior art, it is not indefinite.” Likewise seeVerve LLC v. Crane Cams Inc., 311 F.3d 1116, 65 USPQ2d 1051, 1054 (Fed.Cir. 2002). Expressions such as “substantially” are used in patentdocuments when warranted by the nature of the invention, in order toaccommodate the minor variations that may be appropriate to secure theinvention. Such usage may well satisfy the charge to “particularly pointout and distinctly claim” the invention, 35 U.S.C. §112, and indeed maybe necessary in order to provide the inventor with the benefit of hisinvention. In Andrew Corp. v. Gabriel Elecs. Inc., 847 F.2d 819, 821-22,6 USPQ2d 2010, 2013 (Fed. Cir. 1988) the court explained that usagessuch as “substantially equal” and “closely approximate” may serve todescribe the invention with precision appropriate to the technology andwithout intruding on the prior art. The court again explained in EcolabInc. v. Envirochem, Inc., 264 F.3d 1358, 1367, 60 USPQ2d 1173, 1179(Fed. Cir. 2001) that “like the term ‘about,’ the term ‘substantially’is a descriptive term commonly used in patent claims to ‘avoid a strictnumerical boundary to the specified parameter, see Ecolab Inc. v.Envirochem Inc., 264 F.3d 1358, 60 USPQ2d 1173, 1179 (Fed. Cir. 2001)where the court found that the use of the term “substantially” to modifythe term “uniform” does not render this phrase so unclear such thatthere is no means by which to ascertain the claim scope.

Similarly, other courts have noted that like the term “about,” the term“substantially” is a descriptive term commonly used in patent claims to“avoid a strict numerical boundary to the specified parameter.”, e.g.,see Pall Corp. v. Micron Seps., 66 F.3d 1211, 1217, 36 USPQ2d 1225, 1229(Fed. Cir. 1995); see, e.g., Andrew Corp. v. Gabriel Elecs. Inc., 847F.2d 819, 821-22, 6 USPQ2d 2010, 2013 (Fed. Cir. 1988) (noting thatterms such as “approach each other,” “close to,” “substantially equal,”and “closely approximate” are ubiquitously used in patent claims andthat such usages, when serving reasonably to describe the claimedsubject matter to those of skill in the field of the invention, and todistinguish the claimed subject matter from the prior art, have beenaccepted in patent examination and upheld by the courts). In this case,“substantially” avoids the strict 100% nonuniformity boundary.

Indeed, the foregoing sanctioning of such words of approximation, ascontemplated in the foregoing, has been established as early as 1939,see Ex parte Mallory, 52 USPQ 297, 297 (Pat. Off. Bd. App. 1941) where,for example, the court said “the claims specify that the film is“substantially” eliminated and for the intended purpose, it is believedthat the slight portion of the film which may remain is negligible. Weare of the view, therefore, that the claims may be regarded assufficiently accurate.” Similarly, In re Hutchison, 104 F.2d 829, 42USPQ 90, 93 (C.C.P.A. 1939) the court said “It is realized that“substantial distance” is a relative and somewhat indefinite term, orphrase, but terms and phrases of this character are not uncommon inpatents in cases where, according to the art involved, the meaning canbe determined with reasonable clearness.”

Hence, for at least the forgoing reason, Applicants submit that it isimproper for any examiner to hold as indefinite any claims of thepresent patent that employ any words of approximation.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meanings as commonly understood by one of ordinary skillin the art to which this invention belongs. Preferred methods,techniques, devices, and materials are described, although any methods,techniques, devices, or materials similar or equivalent to thosedescribed herein may be used in the practice or testing of the presentinvention. Structures described herein are to be understood also torefer to functional equivalents of such structures. The presentinvention will be described in detail below with reference toembodiments thereof as illustrated in the accompanying drawings.

References to a “device,” an “apparatus,” a “system,” etc., in thepreamble of a claim should be construed broadly to mean “any structuremeeting the claim terms” exempt for any specific structure(s)/type(s)that has/(have) been explicitly disavowed or excluded oradmitted/implied as prior art in the present specification or incapableof enabling an object/aspect/goal of the invention. Furthermore, wherethe present specification discloses an object, aspect, function, goal,result, or advantage of the invention that a specific prior artstructure and/or method step is similarly capable of performing yet in avery different way, the present invention disclosure is intended to andshall also implicitly include and cover additional correspondingalternative embodiments that are otherwise identical to that explicitlydisclosed except that they exclude such prior art structure(s)/step(s),and shall accordingly be deemed as providing sufficient disclosure tosupport a corresponding negative limitation in a claim claiming suchalternative embodiment(s), which exclude such very different prior artstructure(s)/step(s) way(s).

From reading the present disclosure, other variations and modificationswill be apparent to persons skilled in the art. Such variations andmodifications may involve equivalent and other features which arealready known in the art, and which may be used instead of or inaddition to features already described herein.

Although Claims have been formulated in this Application to particularcombinations of features, it should be understood that the scope of thedisclosure of the present invention also includes any novel feature orany novel combination of features disclosed herein either explicitly orimplicitly or any generalization thereof, whether or not it relates tothe same invention as presently claimed in any Claim and whether or notit mitigates any or all of the same technical problems as does thepresent invention.

Features which are described in the context of separate embodiments mayalso be provided in combination in a single embodiment. Conversely,various features which are, for brevity, described in the context of asingle embodiment, may also be provided separately or in any suitablesubcombination. The Applicants hereby give notice that new Claims may beformulated to such features and/or combinations of such features duringthe prosecution of the present Application or of any further Applicationderived therefrom.

References to “one embodiment,” “an embodiment,” “example embodiment,”“various embodiments,” “some embodiments,” “embodiments of theinvention,” etc., may indicate that the embodiment(s) of the inventionso described may include a particular feature, structure, orcharacteristic, but not every possible embodiment of the inventionnecessarily includes the particular feature, structure, orcharacteristic. Further, repeated use of the phrase “in one embodiment,”or “in an exemplary embodiment,” “an embodiment,” do not necessarilyrefer to the same embodiment, although they may. Moreover, any use ofphrases like “embodiments” in connection with “the invention” are nevermeant to characterize that all embodiments of the invention must includethe particular feature, structure, or characteristic, and should insteadbe understood to mean “at least some embodiments of the invention”includes the stated particular feature, structure, or characteristic.

References to “user”, or any similar term, as used herein, may mean ahuman or non-human user thereof. Moreover, “user”, or any similar term,as used herein, unless expressly stipulated otherwise, is contemplatedto mean users at any stage of the usage process, to include, withoutlimitation, direct user(s), intermediate user(s), indirect user(s), andend user(s). The meaning of “user”, or any similar term, as used herein,should not be otherwise inferred or induced by any pattern(s) ofdescription, embodiments, examples, or referenced prior-art that may (ormay not) be provided in the present patent.

References to “end user”, or any similar term, as used herein, isgenerally intended to mean late stage user(s) as opposed to early stageuser(s). Hence, it is contemplated that there may be a multiplicity ofdifferent types of “end user” near the end stage of the usage process.Where applicable, especially with respect to distribution channels ofembodiments of the invention comprising consumed retailproducts/services thereof (as opposed to sellers/vendors or OriginalEquipment Manufacturers), examples of an “end user” may include, withoutlimitation, a “consumer”, “buyer”, “customer”, “purchaser”, “shopper”,“enjoyer”, “viewer”, or individual person or non-human thing benefitingin any way, directly or indirectly, from use of. or interaction, withsome aspect of the present invention.

In some situations, some embodiments of the present invention mayprovide beneficial usage to more than one stage or type of usage in theforegoing usage process. In such cases where multiple embodimentstargeting various stages of the usage process are described, referencesto “end user”, or any similar term, as used therein, are generallyintended to not include the user that is the furthest removed, in theforegoing usage process, from the final user therein of an embodiment ofthe present invention.

Where applicable, especially with respect to retail distributionchannels of embodiments of the invention, intermediate user(s) mayinclude, without limitation, any individual person or non-human thingbenefiting in any way, directly or indirectly, from use of, orinteraction with, some aspect of the present invention with respect toselling, vending, Original Equipment Manufacturing, marketing,merchandising, distributing, service providing, and the like thereof.

References to “person”, “individual”, “human”, “a party”, “animal”,“creature”, or any similar term, as used herein, even if the context orparticular embodiment implies living user, maker, or participant, itshould be understood that such characterizations are sole by way ofexample, and not limitation, in that it is contemplated that any suchusage, making, or participation by a living entity in connection withmaking, using, and/or participating, in any way, with embodiments of thepresent invention may be substituted by such similar performed by asuitably configured non-living entity, to include, without limitation,automated machines, robots, humanoids, computational systems,information processing systems, artificially intelligent systems, andthe like. It is further contemplated that those skilled in the art willreadily recognize the practical situations where such living makers,users, and/or participants with embodiments of the present invention maybe in whole, or in part, replaced with such non-living makers, users,and/or participants with embodiments of the present invention. Likewise,when those skilled in the art identify such practical situations wheresuch living makers, users, and/or participants with embodiments of thepresent invention may be in whole, or in part, replaced with suchnon-living makers, it will be readily apparent in light of the teachingsof the present invention how to adapt the described embodiments to besuitable for such non-living makers, users, and/or participants withembodiments of the present invention. Thus, the invention is thus toalso cover all such modifications, equivalents, and alternatives fallingwithin the spirit and scope of such adaptations and modifications, atleast in part, for such non-living entities.

Headings provided herein are for convenience and are not to be taken aslimiting the disclosure in any way.

The enumerated listing of items does not imply that any or all of theitems are mutually exclusive, unless expressly specified otherwise.

It is understood that the use of specific component, device and/orparameter names are for example only and not meant to imply anylimitations on the invention. The invention may thus be implemented withdifferent nomenclature/terminology utilized to describe themechanisms/units/structures/components/devices/parameters herein,without limitation. Each term utilized herein is to be given itsbroadest interpretation given the context in which that term isutilized.

Terminology. The following paragraphs provide definitions and/or contextfor terms found in this disclosure (including the appended claims):

“Comprising.” This term is open-ended. As used in the appended claims,this term does not foreclose additional structure or steps. Consider aclaim that recites: “A memory controller comprising a system cache . . ..” Such a claim does not foreclose the memory controller from includingadditional components (e.g., a memory channel unit, a switch).

“Configured To.” Various units, circuits, or other components may bedescribed or claimed as “configured to” perform a task or tasks. In suchcontexts, “configured to” or “operable for” is used to connote structureby indicating that the mechanisms/units/circuits/components includestructure (e.g., circuitry and/or mechanisms) that performs the task ortasks during operation. As such, the mechanisms/unit/circuit/componentcan be said to be configured to (or be operable) for perform(ing) thetask even when the specified mechanisms/unit/circuit/component is notcurrently operational (e.g., is not on). Themechanisms/units/circuits/components used with the “configured to” or“operable for” language include hardware-—for example, mechanisms,structures, electronics, circuits, memory storing program instructionsexecutable to implement the operation, etc. Reciting that amechanism/unit/circuit/component is “configured to” or “operable for”perform(ing) one or more tasks is expressly intended not to invoke 35U.S.C. .sctn.112, sixth paragraph, for thatmechanism/unit/circuit/component. “Configured to” may also includeadapting a manufacturing process to fabricate devices or components thatare adapted to implement or perform one or more tasks

“Based On.” As used herein, this term is used to describe one or morefactors that affect a determination. This term does not forecloseadditional factors that may affect a determination. That is, adetermination may be solely based on those factors or based, at least inpart, on those factors. Consider the phrase “determine A based on B.”While B may be a factor that affects the determination of A, such aphrase does not foreclose the determination of A from also being basedon C. In other instances, A may be determined based solely on B.

The terms “a”, “an” and “the” mean “one or more”, unless expresslyspecified otherwise.

Unless otherwise indicated, all numbers expressing conditions,concentrations, dimensions, and so forth used in the specification andclaims are to be understood as being modified in all instances by theterm “about.” Accordingly, unless indicated to the contrary, thenumerical parameters set forth in the following specification andattached claims are approximations that may vary depending at least upona specific analytical technique.

The term “comprising,” which is synonymous with “including,”“containing,” or “characterized by” is inclusive or open-ended and doesnot exclude additional, unrecited elements or method steps. “Comprising”is a term of art used in claim language which means that the named claimelements are essential, but other claim elements may be added and stillform a construct within the scope of the claim.

As used herein, the phase “consisting of” excludes any element, step, oringredient not specified in the claim. When the phrase “consists of” (orvariations thereof) appears in a clause of the body of a claim, ratherthan immediately following the preamble, it limits only the element setforth in that clause; other elements are not excluded from the claim asa whole. As used herein, the phase “consisting essentially of” and“consisting of” limits the scope of a claim to the specified elements ormethod steps, plus those that do not materially affect the basis andnovel characteristic(s) of the claimed subject matter (see Norian Corp.v Stryker Corp., 363 F.3d 1321, 1331-32, 70 USPQ2d 1508, Fed. Cir.2004). Moreover, for any claim of the present invention which claims anembodiment “consisting essentially of” or “consisting of” a certain setof elements of any herein described embodiment it shall be understood asobvious by those skilled in the art that the present invention alsocovers all possible varying scope variants of any describedembodiment(s) that are each exclusively (i.e., “consisting essentiallyof”) functional subsets or functional combination thereof such that eachof these plurality of exclusive varying scope variants each consistsessentially of any functional subset(s) and/or functional combination(s)of any set of elements of any described embodiment(s) to the exclusionof any others not set forth therein. That is, it is contemplated that itwill be obvious to those skilled how to create a multiplicity ofalternate embodiments of the present invention that simply consistingessentially of a certain functional combination of elements of anydescribed embodiment(s) to the exclusion of any others not set forththerein, and the invention thus covers all such exclusive embodiments asif they were each described herein.

With respect to the terms “comprising,” “consisting of” and “consistingessentially of” where one of these three terms is used herein, thepresently disclosed and claimed subject matter may include the use ofeither of the other two terms. Thus in some embodiments not otherwiseexplicitly recited, any instance of “comprising” may be replaced by“consisting of” or, alternatively, by “consisting essentially of”, andthus, for the purposes of claim support and construction for “consistingof” format claims, such replacements operate to create yet otheralternative embodiments “consisting essentially of” only the elementsrecited in the original “comprising” embodiment to the exclusion of allother elements.

Devices or system modules that are in at least general communicationwith each other need not be in continuous communication with each other,unless expressly specified otherwise. In addition, devices or systemmodules that are in at least general communication with each other maycommunicate directly or indirectly through one or more intermediaries.

A description of an embodiment with several components in communicationwith each other does not imply that all such components are required. Onthe contrary a variety of optional components are described toillustrate the wide variety of possible embodiments of the presentinvention.

As is well known to those skilled in the art many careful considerationsand compromises typically must be made when designing for the optimalmanufacture of a commercial implementation any system, and inparticular, the embodiments of the present invention. A commercialimplementation in accordance with the spirit and teachings of thepresent invention may configured according to the needs of theparticular application, whereby any aspect(s), feature(s), function(s),result(s), component(s), approach(es), or step(s) of the teachingsrelated to any described embodiment of the present invention may besuitably omitted, included, adapted, mixed and matched, or improvedand/or optimized by those skilled in the art, using their average skillsand known techniques, to achieve the desired implementation thataddresses the needs of the particular application.

A “computer” may refer to one or more apparatus and/or one or moresystems that are capable of accepting a structured input, processing thestructured input according to prescribed rules, and producing results ofthe processing as output. Examples of a computer may include: acomputer; a stationary and/or portable computer; a computer having asingle processor, multiple processors, or multi-core processors, whichmay operate in parallel and/or not in parallel; a general purposecomputer; a supercomputer; a mainframe; a super mini-computer; amini-computer; a workstation; a micro-computer; a server; a client; aninteractive television; a web appliance; a telecommunications devicewith internet access; a hybrid combination of a computer and aninteractive television; a portable computer; a tablet personal computer(PC); a personal digital assistant (PDA); a portable telephone;application-specific hardware to emulate a computer and/or software,such as, for example, a digital signal processor (DSP), afield-programmable gate array (FPGA), an application specific integratedcircuit (ASIC), an application specific instruction-set processor(ASIP), a chip, chips, a system on a chip, or a chip set; a dataacquisition device; an optical computer; a quantum computer; abiological computer; and generally, an apparatus that may accept data,process data according to one or more stored software programs, generateresults, and typically include input, output, storage, arithmetic,logic, and control units.

Those of skill in the art will appreciate that where appropriate, someembodiments of the disclosure may be practiced in network computingenvironments with many types of computer system configurations,including personal computers, hand-held devices, multi-processorsystems, microprocessor-based or programmable consumer electronics,network PCs, minicomputers, mainframe computers, and the like. Whereappropriate, embodiments may also be practiced in distributed computingenvironments where tasks are performed by local and remote processingdevices that are linked (either by hardwired links, wireless links, orby a combination thereof) through a communications network. In adistributed computing environment, program modules may be located inboth local and remote memory storage devices.

“Software” may refer to prescribed rules to operate a computer. Examplesof software may include: code segments in one or more computer-readablelanguages; graphical and or/textual instructions; applets; pre-compiledcode; interpreted code; compiled code; and computer programs.

The example embodiments described herein can be implemented in anoperating environment comprising computer-executable instructions (e.g.,software) installed on a computer, in hardware, or in a combination ofsoftware and hardware. The computer-executable instructions can bewritten in a computer programming language or can be embodied infirmware logic. If written in a programming language conforming to arecognized standard, such instructions can be executed on a variety ofhardware platforms and for interfaces to a variety of operating systems.Although not limited thereto, computer software program code forcarrying out operations for aspects of the present invention can bewritten in any combination of one or more suitable programminglanguages, including an object oriented programming languages and/orconventional procedural programming languages, and/or programminglanguages such as, for example, Hypertext Markup Language (HTML),Dynamic HTML, Extensible Markup Language (XML), Extensible StylesheetLanguage (XSL), Document Style Semantics and Specification Language(DSSSL), Cascading Style Sheets (CSS), Synchronized MultimediaIntegration Language (SMIL), Wireless Markup Language (WML), Java™,Jini™, C, C++, Smalltalk, Perl, UNIX Shell, Visual Basic or Visual BasicScript, Virtual Reality Markup Language (VRML), ColdFusion™ or othercompilers, assemblers, interpreters or other computer languages orplatforms.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

A network is a collection of links and nodes (e.g., multiple computersand/or other devices connected together) arranged so that informationmay be passed from one part of the network to another over multiplelinks and through various nodes. Examples of networks include theInternet, the public switched telephone network, the global Telexnetwork, computer networks (e.g., an intranet, an extranet, a local-areanetwork, or a wide-area network), wired networks, and wireless networks.

The Internet is a worldwide network of computers and computer networksarranged to allow the easy and robust exchange of information betweencomputer users. Hundreds of millions of people around the world haveaccess to computers connected to the Internet via Internet ServiceProviders (ISPs). Content providers (e.g., website owners or operators)place multimedia information (e.g., text, graphics, audio, video,animation, and other forms of data) at specific locations on theInternet referred to as webpages. Websites comprise a collection ofconnected, or otherwise related, webpages. The combination of all thewebsites and their corresponding webpages on the Internet is generallyknown as the World Wide Web (WWW) or simply the Web.

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of code, whichcomprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the block may occurout of the order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

Further, although process steps, method steps, algorithms or the likemay be described in a sequential order, such processes, methods andalgorithms may be configured to work in alternate orders. In otherwords, any sequence or order of steps that may be described does notnecessarily indicate a requirement that the steps be performed in thatorder. The steps of processes described herein may be performed in anyorder practical. Further, some steps may be performed simultaneously.

It will be readily apparent that the various methods and algorithmsdescribed herein may be implemented by, e.g., appropriately programmedgeneral purpose computers and computing devices. Typically a processor(e.g., a microprocessor) will receive instructions from a memory or likedevice, and execute those instructions, thereby performing a processdefined by those instructions. Further, programs that implement suchmethods and algorithms may be stored and transmitted using a variety ofknown media.

When a single device or article is described herein, it will be readilyapparent that more than one device/article (whether or not theycooperate) may be used in place of a single device/article. Similarly,where more than one device or article is described herein (whether ornot they cooperate), it will be readily apparent that a singledevice/article may be used in place of the more than one device orarticle.

The functionality and/or the features of a device may be alternativelyembodied by one or more other devices which are not explicitly describedas having such functionality/features. Thus, other embodiments of thepresent invention need not include the device itself.

The term “computer-readable medium” as used herein refers to any mediumthat participates in providing data (e.g., instructions) which may beread by a computer, a processor or a like device. Such a medium may takemany forms, including but not limited to, non-volatile media, volatilemedia, and transmission media. Non-volatile media include, for example,optical or magnetic disks and other persistent memory. Volatile mediainclude dynamic random access memory (DRAM), which typically constitutesthe main memory. Transmission media include coaxial cables, copper wireand fiber optics, including the wires that comprise a system bus coupledto the processor. Transmission media may include or convey acousticwaves, light waves and electromagnetic emissions, such as thosegenerated during radio frequency (RF) and infrared (IR) datacommunications. Common forms of computer-readable media include, forexample, a floppy disk, a flexible disk, hard disk, magnetic tape, anyother magnetic medium, a CD-ROM, DVD, any other optical medium, punchcards, paper tape, any other physical medium with patterns of holes, aRAM, a PROM, an EPROM, a FLASH-EEPROM, removable media, flash memory, a“memory stick”, any other memory chip or cartridge, a carrier wave asdescribed hereinafter, or any other medium from which a computer canread.

Various forms of computer readable media may be involved in carryingsequences of instructions to a processor. For example, sequences ofinstruction (i) may be delivered from RAM to a processor, (ii) may becarried over a wireless transmission medium, and/or (iii) may beformatted according to numerous formats, standards or protocols, such asBluetooth, TDMA, CDMA, 3G.

Where databases are described, it will be understood by one of ordinaryskill in the art that (i) alternative database structures to thosedescribed may be readily employed, (ii) other memory structures besidesdatabases may be readily employed. Any schematic illustrations andaccompanying descriptions of any sample databases presented herein areexemplary arrangements for stored representations of information. Anynumber of other arrangements may be employed besides those suggested bythe tables shown. Similarly, any illustrated entries of the databasesrepresent exemplary information only; those skilled in the art willunderstand that the number and content of the entries can be differentfrom those illustrated herein. Further, despite any depiction of thedatabases as tables, an object-based model could be used to store andmanipulate the data types of the present invention and likewise, objectmethods or behaviors can be used to implement the processes of thepresent invention.

A “computer system” may refer to a system having one or more computers,where each computer may include a computer-readable medium embodyingsoftware to operate the computer or one or more of its components.Examples of a computer system may include: a distributed computer systemfor processing information via computer systems linked by a network; twoor more computer systems connected together via a network fortransmitting and/or receiving information between the computer systems;a computer system including two or more processors within a singlecomputer; and one or more apparatuses and/or one or more systems thatmay accept data, may process data in accordance with one or more storedsoftware programs, may generate results, and typically may includeinput, output, storage, arithmetic, logic, and control units.

A “network” may refer to a number of computers and associated devicesthat may be connected by communication facilities. A network may involvepermanent connections such as cables or temporary connections such asthose made through telephone or other communication links. A network mayfurther include hard-wired connections (e.g., coaxial cable, twistedpair, optical fiber, waveguides, etc.) and/or wireless connections(e.g., radio frequency waveforms, free-space optical waveforms, acousticwaveforms, etc.). Examples of a network may include: an internet, suchas the Internet; an intranet; a local area network (LAN); a wide areanetwork (WAN); and a combination of networks, such as an internet and anintranet.

As used herein, the “client-side” application should be broadlyconstrued to refer to an application, a page associated with thatapplication, or some other resource or function invoked by a client-siderequest to the application. A “browser” as used herein is not intendedto refer to any specific browser (e.g., Internet Explorer, Safari,FireFox, or the like), but should be broadly construed to refer to anyclient-side rendering engine that can access and displayInternet-accessible resources. A “rich” client typically refers to anon-HTTP based client-side application, such as an SSH or CFIS client.Further, while typically the client-server interactions occur usingHTTP, this is not a limitation either. The client server interaction maybe formatted to conform to the Simple Object Access Protocol (SOAP) andtravel over HTTP (over the public Internet), FTP, or any other reliabletransport mechanism (such as IBM® MQSeries® technologies and CORBA, fortransport over an enterprise intranet) may be used. Any application orfunctionality described herein may be implemented as native code, byproviding hooks into another application, by facilitating use of themechanism as a plug-in, by linking to the mechanism, and the like.

Exemplary networks may operate with any of a number of protocols, suchas Internet protocol (IP), asynchronous transfer mode (ATM), and/orsynchronous optical network (SONET), user datagram protocol (UDP), IEEE802.x, etc.

Embodiments of the present invention may include apparatuses forperforming the operations disclosed herein. An apparatus may bespecially constructed for the desired purposes, or it may comprise ageneral-purpose device selectively activated or reconfigured by aprogram stored in the device.

Embodiments of the invention may also be implemented in one or acombination of hardware, firmware, and software. They may be implementedas instructions stored on a machine-readable medium, which may be readand executed by a computing platform to perform the operations describedherein.

More specifically, as will be appreciated by one skilled in the art,aspects of the present invention may be embodied as a system, method orcomputer program product. Accordingly, aspects of the present inventionmay take the form of an entirely hardware embodiment, an entirelysoftware embodiment (including firmware, resident software, micro-code,etc.) or an embodiment combining software and hardware aspects that mayall generally be referred to herein as a “circuit,” “module” or“system.” Furthermore, aspects of the present invention may take theform of a computer program product embodied in one or more computerreadable medium(s) having computer readable program code embodiedthereon.

In the following description and claims, the terms “computer programmedium” and “computer readable medium” may be used to generally refer tomedia such as, but not limited to, removable storage drives, a hard diskinstalled in hard disk drive, and the like. These computer programproducts may provide software to a computer system. Embodiments of theinvention may be directed to such computer program products.

An algorithm is here, and generally, considered to be a self-consistentsequence of acts or operations leading to a desired result. Theseinclude physical manipulations of physical quantities. Usually, thoughnot necessarily, these quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared, and otherwise manipulated. It has proven convenient at times,principally for reasons of common usage, to refer to these signals asbits, values, elements, symbols, characters, terms, numbers or the like.It should be understood, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities.

Unless specifically stated otherwise, and as may be apparent from thefollowing description and claims, it should be appreciated thatthroughout the specification descriptions utilizing terms such as“processing,” “computing,” “calculating,” “determining,” or the like,refer to the action and/or processes of a computer or computing system,or similar electronic computing device, that manipulate and/or transformdata represented as physical, such as electronic, quantities within thecomputing system's registers and/or memories into other data similarlyrepresented as physical quantities within the computing system'smemories, registers or other such information storage, transmission ordisplay devices.

Additionally, the phrase “configured to” or “operable for” can includegeneric structure (e.g., generic circuitry) that is manipulated bysoftware and/or firmware (e.g., an FPGA or a general-purpose processorexecuting software) to operate in a manner that is capable of performingthe task(s) at issue. “Configured to” may also include adapting amanufacturing process (e.g., a semiconductor fabrication facility) tofabricate devices (e.g., integrated circuits) that are adapted toimplement or perform one or more tasks.

In a similar manner, the term “processor” may refer to any device orportion of a device that processes electronic data from registers and/ormemory to transform that electronic data into other electronic data thatmay be stored in registers and/or memory. A “computing platform” maycomprise one or more processors.

Embodiments within the scope of the present disclosure may also includetangible and/or non-transitory computer-readable storage media forcarrying or having computer-executable instructions or data structuresstored thereon. Such non-transitory computer-readable storage media canbe any available media that can be accessed by a general purpose orspecial purpose computer, including the functional design of any specialpurpose processor as discussed above. By way of example, and notlimitation, such non-transitory computer-readable media can include RAM,ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storageor other magnetic storage devices, or any other medium which can be usedto carry or store desired program code means in the form ofcomputer-executable instructions, data structures, or processor chipdesign. When information is transferred or provided over a network oranother communications connection (either hardwired, wireless, orcombination thereof) to a computer, the computer properly views theconnection as a computer-readable medium. Thus, any such connection isproperly termed a computer-readable medium. Combinations of the aboveshould also be included within the scope of the computer-readable media.

While a non-transitory computer readable medium includes, but is notlimited to, a hard drive, compact disc, flash memory, volatile memory,random access memory, magnetic memory, optical memory, semiconductorbased memory, phase change memory, optical memory, periodicallyrefreshed memory, and the like; the non-transitory computer readablemedium, however, does not include a pure transitory signal per se; i.e.,where the medium itself is transitory.

Many embodiments, and variations thereof, may provide for machinelearning methods and means for learning from, but not limited to, tags,barcodes, human entering IDs, QR codes, radio-frequency identification(RFID)s, etc. In some embodiments, a machine learning system mayidentify an item associated with a database. The machine learning systemmay classify the item by obtaining an image or images from, withoutlimitation, a camera. The system may classify the item to match adatabase by, but not limited to, ID, barcode or QR code etc.

Some embodiments, may be implemented in a component such as, but notlimited to, a smart device with a scanner and a camera capability.Through a network connection, the smart device may send barcode andassociated images to a cloud computing system. The cloud computingsystem may train a neural network or machine learning database. In someembodiments, the smart device may be incorporated in a robotic likedevice. Some embodiments may be implemented for recognizing an ID of acustomer.

In some embodiments, a POS system may include, without limitation, ascanner component, which may scan, but not limited to, barcodes, QRcodes, RFIDs, IDs, etc., a camera component, which may capture one ormore images of objects, and a computing component with a predictionalgorithm to classify the object. In some embodiments, the POS systemmay include one or more database for, but not limited to, a point ofsale, a point of return or customer order, aisle number and map ofitems, inventory management, customer relationship management (CRM),financials and warehousing, etc. In some embodiments, the POS system mayinclude a training database in a server system for training a neuralnetwork or artificial intelligence algorithm. In some embodiments, thePOS system may include a database for storing a POS database for suchas, but not limited to, calculating total price, making a payment inexchange for goods, issuing a receipt for the transaction, inventorymanagement, CRM, financials and warehousing, etc. In some embodiments,the POS system may include an image database for, but not limited to,supervised training. In some embodiments, the POS system may include adatabase for storing aisle number and floor map of items. In someembodiments, the POS system may function to display the aisle number andfloor map of searched items. In some embodiments, the POS system mayfunction to compare an aisle number and floor maps of an object to adatabase to see whether it is misplaced or not.

Some embodiments may be incorporated with a surveillance system. In someembodiments, the surveillance system may incorporate supervisedlearning. In some embodiments, the surveillance system may incorporateunsupervised learning. In some embodiments, the surveillance system mayinclude, but not limited to: one or more devices that may include, butnot limited to, a camera unit for capturing images, and a computing unitfor capturing images/videos to identify names of the people; a serversystem for training on images/video and uploading neural weights to theone or more devices; and a network for linking the devices and theserver system. In some embodiments, the surveillance system may find asimilarity of a certain signature or image and report the location.

FIG. 1 illustrates an exemplary flow diagram for machine learning, inaccordance with an embodiment of the present invention. In the presentembodiment, a system 100 such as, without limitation, a POS system, mayhave a scanner system 120 for scanning identification means of an itemor object such as, but not limited to, tags, barcodes, QR codes, RFIDs,etc. Scanner system 120 may also include an imaging device such as, butnot limited to, a camera for capturing one or more images of the item.The scanned identification may be used to obtain an ID 110 for theobject from a database 105. The ID for the item and the captured imagesmay then be transferred to training system 115. The images associatedwith the ID may be stored into an images database. In some embodiments,a supervised training method may be used to train a neural networksystem. After, the training system 115 obtains enough images for eachclassification items and runs enough epochs (iterations) for thetraining. As a non-limiting example, this process may take hours ordays. Later, the training system 115 may send trained synaptic weightsto prediction module 125 to predict the classification of new objects.An error rate 130 may be determined from comparing the ID 110 fromidentification means and ID from prediction module 125. When the errorrate is at an acceptable level, the prediction module 125 may be used toclassify objects instead of scanning identification means to identifythe object.

FIG. 2 illustrates an exemplary flow diagram for machine learning withkey entry, in accordance with an embodiment of the present invention. Inthe present embodiment, a system 200 such as, without limitation, a POSsystem, may have a scanner system 220 for scanning identification meansof an item or object such as, but not limited to, tags, barcodes, QRcodes, RFIDs, etc. Scanner system 220 may also include an imaging devicesuch as, but not limited to, a camera for capturing one or more imagesof the item. If the scanner system 220 obtained an identification 222,the scanned identification may be used to obtain an ID 210 for theobject from a database 205. If the scanner system 220 did not obtainedan identification 222, a manual entry 224 may be used to obtain an ID210 for the object from a database 205. The ID for the item and thecaptured images may then be transferred to training system 215. Theimages associated with the ID may be stored into an images database. Insome embodiments, a supervised training method may be used to train aneural network system. After, the training system 215 obtains enoughimages for each classification items and runs enough epochs for thetraining. As a non-limiting example, this process may take hours ordays. Later, the training system 215 may send trained synaptic weightsto prediction module 225 to predict the classification of new objects.An error rate 230 may be determined from comparing the ID 210 fromidentification means and ID from prediction module 225. When the errorrate is at an acceptable level, the prediction module 225 may be used toclassify objects instead of scanning identification means to identifythe object.

In some embodiments, a supervised learning problem may have access tolabeled training examples (x(i), y(i)) where x is an input image and yis a classification ID. A training model may use a softmax regressionmodel (or multinomial logistic regression). A fixed training set may be:{(x⁽¹⁾,y⁽¹⁾, . . . , (x^((m)),y^((m)))} of m labeled examples where theinput features are: x^((i))ε

^(n+1).

The present example may use a notational convention of letting thefeature vectors x be n+1 dimensional, with x0=1 corresponding to theintercept term. With logistic regression in the binary classificationsetting, the labels may be:

y^((i))ε{0,1}

A hypothesis may take the form:

${{h_{\theta}(x)} = \frac{1}{1 + {\exp \left( {{- \theta^{T}}x} \right)}}},$

Given a training set of m examples, an overall cost function may bedefined to be the model parameters θ were trained to minimize the costfunction:

${J(\theta)} = {- {\frac{1}{m}\left\lbrack {{\sum\limits_{i = 1}^{m}\; {y^{(i)}\log \; {h_{\theta}\left( x^{(i)} \right)}}} + {\left( {1 - y^{(i)}} \right){\log \left( {1 - {h_{\theta}\left( x^{(i)} \right)}} \right)}}} \right\rbrack}}$

One iteration of batch gradient descent may be implemented as follows:

${\nabla_{\theta \; j}{J(\theta)}} = {{- \frac{1}{m}}{\sum\limits_{i = 1}^{m}\; \left\lbrack {x^{(i)}\left( {{1\left\{ {y^{(i)} = j} \right\}} - {p\left( {{y^{(i)} = {jx^{(i)}}};\theta} \right)}} \right)} \right\rbrack}}$

The cost function may be modified by adding a weight decay term:

$\frac{\lambda}{2}{\sum_{i = 1}^{k}{\sum_{j = 0}^{n}\theta_{ij}^{2}}}$

which may penalize large values of the parameters. The cost function isnow:

${J\; \theta} = {{- {\frac{1}{m}\left\lbrack {\sum\limits_{i = 1}^{m}\; {\sum\limits_{j = 1}^{k}\; {1\left\{ {y^{(i)} = j} \right\} \log \frac{^{\theta_{j}^{T}x^{(i)}}}{\sum_{i = 1}^{k}^{\theta_{i}^{T}x^{(i)}}}}}} \right\rbrack}} + {\frac{\lambda}{2}{\sum\limits_{i = 1}^{k}\; {\sum\limits_{j = 0}^{n}\theta_{ij}^{2}}}}}$

With this weight decay term (for any λ>0), the cost function J(θ) may benow strictly convex, and may be guaranteed to have a unique solution.The Hessian is now invertible, and because J(θ) is convex, algorithmssuch as gradient descent, L-BFGS, etc. are guaranteed to converge to theglobal minimum.

To apply an optimization algorithm, a derivative of this new definitionof J(θ) may be needed. One may show that the derivative is:

${\nabla_{\theta \; j}{J(\theta)}} = {{{- \frac{1}{m}}{\sum\limits_{i = 1}^{m}\; \left\lbrack {x^{(i)}\left( {{1\left\{ {y^{(i)} = j} \right\}} - {p\left( {{y^{(i)} = {jx^{(i)}}};\theta} \right)}} \right)} \right\rbrack}} + {\lambda\theta}_{j}}$

By minimizing J(θ) with respect to θ, one may have a workingimplementation of softmax regression.

To train the neural network, one may now repeatedly take iterations ofgradient descent to reduce the cost function J(θ). After trainingprocess, one may reduce the value of cost function after running backpropagation for many Enter ID (iterations). When the error rate achievesto an acceptable level, the system may store the neural weights forprediction process.

By using the neural weights in the prediction process, one may predictobjects in prediction module 125 or 225. In a non-limiting example,prediction module 125 or 225 may predict it is a gala apple and a kindof apple. Each kind of apple may have an object ID number associatedwith price, the POS system may calculate the weight and the price.

FIG. 3 illustrates an exemplary prediction, in accordance with anembodiment of the present invention. In the present prediction example300, a captured image 335 may be predicted by prediction module 125 or225 and may predict it is a gala apple and a kind of apple 350. Similarimages 340 may be stored in training system 115 or 215. Each kind ofapple may have an object ID number 345 associated with price, the POSsystem may calculate the weight and the price.

FIG. 4 illustrates an exemplary neural network architecture, inaccordance with an embodiment of the present invention. A neural network400 may include a plurality of multiple stages of subset neural layers465, a plurality of stages of fully connected layers 470, and a finalstage of a classifier layer 475. Subset neural network 465 may have aconvolutional layer 455 and a pooling layer 460. Convolutional layer 455may compute a forward pass and a backpropagation pass, and may havetrainable filters and one trainable bias per feature map. A hyperbolictangent or rectified function may be applied to activations in thislayer. In convolutional layers 455, each map may be connected to all ofits preceding feature maps. The purpose of pooling layers 460 may be toachieve spatial invariance by reducing a resolution of the feature maps.Each pooled feature map may correspond to one feature map of theprevious layer. The unit of the pooling layer may be an n x n patch sizewindow. The pooling window may be of arbitrary size, and windows may beoverlapping.

One may evaluate two different pooling operations: max pooling andsubsampling.

The subsampling function:

$a_{j} = {\tanh\left( {{\beta {\sum\limits_{N \times N}\; a_{i}^{n \times n}}} + b} \right)}$

takes the average over the inputs, multiplies it with a trainablescalar, adds a trainable bias b, and passes the result through thenon-linearity. The max pooling function:

$a_{j} = {\max\limits_{N \times N}\left( {a_{i}^{n \times n}{u\left( {n,n} \right)}} \right)}$

applies a window function u(x; y) to the input patch, and computes themaximum in the neighborhood. In both cases, the result may be a featuremap of lower resolution.

The training may use the forward propagation and backpropagationalgorithm. For error propagation and weight adaption in convolutionallayers 455, pooling layers 460, fully connected layers 470, andclassifier layer 475 may use a standard procedure of neural networks.After training neural network 400 with the image database, the systemmay calculate proper weights which may be used to classify the objects.

In some embodiments, the machine learning system may link with storelayouts with aisles number, and may cooperate with a mobile robot suchas, without limitation, having wheels or legs with camera to navigateaisle by aisle. In the present embodiment, the machine learning systemmay map items with the layout of the store. In some embodiments, usingthe map of items, a navigation system may help shoppers find what theywant. In some embodiments, the system may be be able to display aislenumbers and floor map for searched items. In some embodiments, the robotwith deep learning or machine learning algorithm may identify themisplaced items. In some embodiments, the robot may either put themisplaced items to the right place, or report a record of misplaceditems and locations. In some alternate embodiments, the machine learningsystem may be used in warehouse systems.

In some embodiments, the machine learning system may be utilized torecognize a name of a customer. Some store cards may have at least one,barcode, QR code magnetic strip, or credit card number associated withpersonal name. As a non-limiting example, whenever the customer mayenter the store the system may scan the store card and capture images ofthe customer. The system may send (image, id) as a pair to a database.Later, the system may have enough data to train a neural network torecognize the ID of the image. The system may use the ID to look up thename in the store-card's database to recognize the name of the customerfrom an image.

FIG. 5 illustrates an exemplary integrated machine learning system, inaccordance with an embodiment of the present invention. In the presentembodiment, an integrated system 500 such as, without limitation, asmart device, may have a scanner system 520 for scanning identificationmeans of an item or object such as, but not limited to, tags, barcodes,QR codes, RFIDs, etc. Scanner system 520 may also include an imagingdevice such as, but not limited to, a camera for capturing one or moreimages of the item. The scanned identification may be used to obtain anID 510 for the object from a database such as, without limitation, a POSdatabase using a network connection 507. The ID for the item and thecaptured images may then be transferred to training system 515. Theimages associated with the ID may be stored into an images database.Training system 515 may send trained synaptic weights to predictionmodule 525 to predict the classification of new objects. An error rate530 may be determined from comparing the ID 510 from identificationmeans and ID from prediction module 525. When the error rate is at anacceptable level, the prediction module 525 may be used to classifyobjects instead of scanning identification means to identify the objectand send the identification to the POS system using network connection507.

FIG. 6 illustrates an exemplary device with scanner and cameracapability, in accordance with an embodiment of the present invention.In the present embodiment, device 600 such as, without limitation, asmart device, may have a display 632 and sensors 624 for scanningidentification means of an item or object such as, but not limited to,tags, barcodes, QR codes, RFIDs, etc., and an imaging device such as,but not limited to, a camera for capturing one or more images of theitem. Device 600 may communicate with a server system, such as, withoutlimitation, a cloud computing system. In operation, device 600 may sendthe cloud computing system for example, without limitation, IDs andassociated images to the cloud computing system. The cloud computingsystem may train a neural network or machine learning database forrecognition capability by using a supervision training algorithm. Aftertraining, the cloud computing system sends back neural weights to device600 for object recognition. In some embodiments, the machine learningsystem may link with store layouts or maps with aisles number, and maycooperate with a mobile robot such as, without limitation, having wheelsor legs with camera to navigate aisle by aisle. In a non-limitingexample, the robot may move around and capture 2D or 3D map andenvironment. With the function to recognize the IDs of goods and tocalculate the aisle location and map, device 600 may compare to theaisle map in the store's database whether it is in the right location ornot.

FIG. 7 illustrates an exemplary block diagram of a device with scannerand camera capability, in accordance with an embodiment of the presentinvention. In the present embodiment, device 700 such as, withoutlimitation, a smart device, may have a network connection 707, a scanner721 for scanning identification means of an item or object such as, butnot limited to, tags, barcodes, QR codes, RFIDs, etc., an imaging device723 such as, but not limited to, a camera for capturing one or moreimages of the item, and a prediction module 727. Device 700 maycommunicate with a server system, such as, without limitation, a cloudcomputing system. In operation, device 700 may send the cloud computingsystem for example, without limitation, IDs and associated images to thecloud computing system. The cloud computing system may train a neuralnetwork or machine learning database for recognition capability by usinga supervision training algorithm. After training, the cloud computingsystem sends back neural weights to prediction module 727 for objectrecognition.

FIG. 8 illustrates an exemplary block diagram of a surveillance system,in the prior art. Typically, traditional surveillance systems 800 mayuse a device 806 to capture images with or without motion detection.Those images may be uploaded to a network server 811 for later analysisby a machine or a human.

FIG. 9 illustrates an exemplary block diagram of a surveillance system,in accordance with an embodiment of the present invention. In thepresent embodiment, system 900 may include a device 907 to captureimages within a surveilled area. Captured images may be sent to anetwork server system 912. The present embodiment may learn how toclassify an object by using a plurality of images from an existingdatabase or capturing images from cameras. Neural weights may be learnedfrom server system 912. System 912 may upload the neural weights todevice 907. Then device 907 may classify the objects by using parametersthat may be calculated by using neural weights. If device 907 cannotclassify an object with higher score, device 907 may ask forinstructions from a human or any other help. After giving instruction orhelp, device 907 may do a small learning in device or send to server912. If device 907 sends the images with a low probability to server912, then server 912 may try to learn from the images/video again andupdate the neural weights to device 907. Thus, the system may increasethe accuracy.

Some embodiments may use a supervised learning. As a non-limitingexample, the system 900 may provide labeled personal images in acompany. The neural network may be trained to classify names of peopleby using a tagged/labeled database. Company security may check who is infront of camera.

Some embodiments may use an unsupervised training where people may beidentified thru unsupervised learning. In some embodiments, the learningmay run on device 907. In some embodiments, images may be uploaded toserver 912 for unsupervised learning. Because of non-labeled images,device 907 or server 912 may just classify to different people withoutthe proper name. As a non-limiting example, images may be classifiedinto people-0, people-1, etc. In some embodiments, names may also beregistered to put names on after the device has classified to people-0or people-1, etc.

FIG. 10 illustrates an exemplary block diagram of a surveillance system,in accordance with an embodiment of the present invention. In thepresent embodiment, system 1000 may include a device 1008 to captureimages within a surveilled area. Device 1008 may do a partial training.Classified labels may be sent to a network server system 1013 to reducecommunication bandwidth.

FIG. 11 illustrates an exemplary method for a search, in accordance withan embodiment of the present invention. Referring to FIG. 9 or FIG. 10,in the present embodiment, a search for, but not limited to, an image ora signature may begin at a step 1102 where network server system 912 or1013 may send to device 907 or 1008 the image or signature for thesearch. In a step 1100 device 907 or 1008 may attempt to detect asimilarity of the image or signature in a captured image or signature.In a step 1106, if device 907 or 1008 may not find a high probability ofdetection the process may return to step 1104. If device 907 or 1008 mayfind a high probability of detection, device 907 or 1008 may reporttheir network location to network server system 912 or 1013. In anon-limiting example, to find a person named “Peter” the system mayupload a picture or signature of “Peter” from network server system 912or 1013 to device 907 or 1008, then the system may ask device 907 or1008 to find “Peter”. Once the device 907 or 1008 finds “Peter”, thedevice 907 or 1008 may send a message to network server system 912 or1013. Network server system 912 or 1013 may know which device andlocation of the device. The system may identify the location of “Peter”.

Those skilled in the art will readily recognize, in light of and inaccordance with the teachings of the present invention, that any of theforegoing steps and/or system modules may be suitably replaced,reordered, removed and additional steps and/or system modules may beinserted depending upon the needs of the particular application, andthat the systems of the foregoing embodiments may be implemented usingany of a wide variety of suitable processes and system modules, and isnot limited to any particular computer hardware, software, middleware,firmware, microcode and the like. For any method steps described in thepresent application that can be carried out on a computing machine, atypical computer system can, when appropriately configured or designed,serve as a computer system in which those aspects of the invention maybe embodied.

FIG. 12 is a block diagram depicting an exemplary client/server systemwhich may be used by an exemplary web-enabled/networked embodiment ofthe present invention.

A communication system 1200 includes a multiplicity of clients with asampling of clients denoted as a client 1202 and a client 1204, amultiplicity of local networks with a sampling of networks denoted as alocal network 1206 and a local network 1208, a global network 1210 and amultiplicity of servers with a sampling of servers denoted as a server1212 and a server 1214.

Client 1202 may communicate bi-directionally with local network 1206 viaa communication channel 1216. Client 1204 may communicatebi-directionally with local network 1208 via a communication channel1218. Local network 1206 may communicate bi-directionally with globalnetwork 1210 via a communication channel 1220. Local network 1208 maycommunicate bi-directionally with global network 1210 via acommunication channel 1222. Global network 1210 may communicatebi-directionally with server 1212 and server 1214 via a communicationchannel 1224. Server 1212 and server 1214 may communicatebi-directionally with each other via communication channel 1224.Furthermore, clients 1202, 1204, local networks 1206, 1208, globalnetwork 1210 and servers 1212, 1214 may each communicatebi-directionally with each other.

In one embodiment, global network 1210 may operate as the Internet. Itwill be understood by those skilled in the art that communication system1200 may take many different forms. Non-limiting examples of forms forcommunication system 1200 include local area networks (LANs), wide areanetworks (WANs), wired telephone networks, wireless networks, or anyother network supporting data communication between respective entities.

Clients 1202 and 1204 may take many different forms. Non-limitingexamples of clients 1202 and 1204 include personal computers, personaldigital assistants (PDAs), cellular phones and smartphones.

Client 1202 includes a CPU 1226, a pointing device 1228, a keyboard1230, a microphone 1232, a printer 1234, a memory 1236, a mass memorystorage 1238, a GUI 1240, a video camera 1242, an input/output interface1244 and a network interface 1246.

CPU 1226, pointing device 1228, keyboard 1230, microphone 1232, printer1234, memory 1236, mass memory storage 1238, GUI 1240, video camera1242, input/output interface 1244 and network interface 1246 maycommunicate in a unidirectional manner or a bi-directional manner witheach other via a communication channel 1248. Communication channel 1248may be configured as a single communication channel or a multiplicity ofcommunication channels.

CPU 1226 may be comprised of a single processor or multiple processors.CPU 1226 may be of various types including micro-controllers (e.g., withembedded RAM/ROM) and microprocessors such as programmable devices(e.g., RISC or SISC based, or CPLDs and FPGAs) and devices not capableof being programmed such as gate array ASICs (Application SpecificIntegrated Circuits) or general purpose microprocessors.

As is well known in the art, memory 1236 is used typically to transferdata and instructions to CPU 1226 in a bi-directional manner. Memory1236, as discussed previously, may include any suitablecomputer-readable media, intended for data storage, such as thosedescribed above excluding any wired or wireless transmissions unlessspecifically noted. Mass memory storage 1238 may also be coupledbi-directionally to CPU 1226 and provides additional data storagecapacity and may include any of the computer-readable media describedabove. Mass memory storage 1238 may be used to store programs, data andthe like and is typically a secondary storage medium such as a harddisk. It will be appreciated that the information retained within massmemory storage 1238, may, in appropriate cases, be incorporated instandard fashion as part of memory 1236 as virtual memory.

CPU 1226 may be coupled to GUI 1240. GUI 1240 enables a user to view theoperation of computer operating system and software. CPU 1226 may becoupled to pointing device 1228. Non-limiting examples of pointingdevice 1228 include computer mouse, trackball and touchpad. Pointingdevice 1228 enables a user with the capability to maneuver a computercursor about the viewing area of GUI 1240 and select areas or featuresin the viewing area of GUI 1240. CPU 1226 may be coupled to keyboard1230. Keyboard 1230 enables a user with the capability to inputalphanumeric textual information to CPU 1226. CPU 1226 may be coupled tomicrophone 1232. Microphone 1232 enables audio produced by a user to berecorded, processed and communicated by CPU 1226. CPU 1226 may beconnected to printer 1234. Printer 1234 enables a user with thecapability to print information to a sheet of paper. CPU 1226 may beconnected to video camera 1242. Video camera 1242 enables video producedor captured by user to be recorded, processed and communicated by CPU1226.

CPU 1226 may also be coupled to input/output interface 1244 thatconnects to one or more input/output devices such as such as CD-ROM,video monitors, track balls, mice, keyboards, microphones,touch-sensitive displays, transducer card readers, magnetic or papertape readers, tablets, styluses, voice or handwriting recognizers, orother well-known input devices such as, of course, other computers.

Finally, CPU 1226 optionally may be coupled to network interface 1246which enables communication with an external device such as a databaseor a computer or telecommunications or internet network using anexternal connection shown generally as communication channel 1216, whichmay be implemented as a hardwired or wireless communications link usingsuitable conventional technologies. With such a connection, CPU 1226might receive information from the network, or might output informationto a network in the course of performing the method steps described inthe teachings of the present invention.

It will be further apparent to those skilled in the art that at least aportion of the novel method steps and/or system components of thepresent invention may be practiced and/or located in location(s)possibly outside the jurisdiction of the United States of America (USA),whereby it will be accordingly readily recognized that at least a subsetof the novel method steps and/or system components in the foregoingembodiments must be practiced within the jurisdiction of the USA for thebenefit of an entity therein or to achieve an object of the presentinvention. Thus, some alternate embodiments of the present invention maybe configured to comprise a smaller subset of the foregoing means forand/or steps described that the applications designer will selectivelydecide, depending upon the practical considerations of the particularimplementation, to carry out and/or locate within the jurisdiction ofthe USA. For example, any of the foregoing described method steps and/orsystem components which may be performed remotely over a network (e.g.,without limitation, a remotely located server) may be performed and/orlocated outside of the jurisdiction of the USA while the remainingmethod steps and/or system components (e.g., without limitation, alocally located client) of the forgoing embodiments are typicallyrequired to be located/performed in the USA for practicalconsiderations. In client-server architectures, a remotely locatedserver typically generates and transmits required information to a USbased client, for use according to the teachings of the presentinvention. Depending upon the needs of the particular application, itwill be readily apparent to those skilled in the art, in light of theteachings of the present invention, which aspects of the presentinvention can or should be located locally and which can or should belocated remotely. Thus, for any claims construction of the followingclaim limitations that are construed under 35 USC §112 (6) it isintended that the corresponding means for and/or steps for carrying outthe claimed function are the ones that are locally implemented withinthe jurisdiction of the USA, while the remaining aspect(s) performed orlocated remotely outside the USA are not intended to be construed under35 USC §112 (6). In some embodiments, the methods and/or systemcomponents which may be located and/or performed remotely include,without limitation: It is noted that according to USA law, all claimsmust be set forth as a coherent, cooperating set of limitations thatwork in functional combination to achieve a useful result as a whole.Accordingly, for any claim having functional limitations interpretedunder 35 USC §112 (6) where the embodiment in question is implemented asa client-server system with a remote server located outside of the USA,each such recited function is intended to mean the function ofcombining, in a logical manner, the information of that claim limitationwith at least one other limitation of the claim. For example, inclient-server systems where certain information claimed under 35 USC§112 (6) is/(are) dependent on one or more remote servers locatedoutside the USA, it is intended that each such recited function under 35USC §112 (6) is to be interpreted as the function of the local systemreceiving the remotely generated information required by a locallyimplemented claim limitation, wherein the structures and or steps whichenable, and breath life into the expression of such functions claimedunder 35 USC §112 (6) are the corresponding steps and/or means locatedwithin the jurisdiction of the USA that receive and deliver thatinformation to the client (e.g., without limitation, client-sideprocessing and transmission networks in the USA). When this applicationis prosecuted or patented under a jurisdiction other than the USA, then“USA” in the foregoing should be replaced with the pertinent country orcountries or legal organization(s) having enforceable patentinfringement jurisdiction over the present application, and “35 USC §112(6)” should be replaced with the closest corresponding statute in thepatent laws of such pertinent country or countries or legalorganization(s).

All the features disclosed in this specification, including anyaccompanying abstract and drawings, may be replaced by alternativefeatures serving the same, equivalent or similar purpose, unlessexpressly stated otherwise. Thus, unless expressly stated otherwise,each feature disclosed is one example only of a generic series ofequivalent or similar features.

It is noted that according to USA law 35 USC §112 (1), all claims mustbe supported by sufficient disclosure in the present patentspecification, and any material known to those skilled in the art neednot be explicitly disclosed. However, 35 USC §112 (6) requires thatstructures corresponding to functional limitations interpreted under 35USC §112 (6) must be explicitly disclosed in the patent specification.Moreover, the USPTO's Examination policy of initially treating andsearching prior art under the broadest interpretation of a “mean for”claim limitation implies that the broadest initial search on 112(6)functional limitation would have to be conducted to support a legallyvalid Examination on that USPTO policy for broadest interpretation of“mean for” claims. Accordingly, the USPTO will have discovered amultiplicity of prior art documents including disclosure of specificstructures and elements which are suitable to act as correspondingstructures to satisfy all functional limitations in the below claimsthat are interpreted under 35 USC §112 (6) when such correspondingstructures are not explicitly disclosed in the foregoing patentspecification. Therefore, for any invention element(s)/structure(s)corresponding to functional claim limitation(s), in the below claimsinterpreted under 35 USC §112 (6), which is/are not explicitly disclosedin the foregoing patent specification, yet do exist in the patent and/ornon-patent documents found during the course of USPTO searching,Applicant(s) incorporate all such functionally corresponding structuresand related enabling material herein by reference for the purpose ofproviding explicit structures that implement the functional meansclaimed. Applicant(s) request(s) that fact finders during any claimsconstruction proceedings and/or examination of patent allowabilityproperly identify and incorporate only the portions of each of thesedocuments discovered during the broadest interpretation search of 35 USC§112 (6) limitation, which exist in at least one of the patent and/ornon-patent documents found during the course of normal USPTO searchingand or supplied to the USPTO during prosecution. Applicant(s) alsoincorporate by reference the bibliographic citation information toidentify all such documents comprising functionally correspondingstructures and related enabling material as listed in any PTO Form-892or likewise any information disclosure statements (IDS) entered into thepresent patent application by the USPTO or Applicant(s) or any 3^(rd)parties. Applicant(s) also reserve its right to later amend the presentapplication to explicitly include citations to such documents and/orexplicitly include the functionally corresponding structures which wereincorporate by reference above.

Thus, for any invention element(s)/structure(s) corresponding tofunctional claim limitation(s), in the below claims, that areinterpreted under 35 USC §112 (6), which is/are not explicitly disclosedin the foregoing patent specification, Applicant(s) have explicitlyprescribed which documents and material to include the otherwise missingdisclosure, and have prescribed exactly which portions of such patentand/or non-patent documents should be incorporated by such reference forthe purpose of satisfying the disclosure requirements of 35 USC §112(6). Applicant(s) note that all the identified documents above which areincorporated by reference to satisfy 35 USC §112 (6) necessarily have afiling and/or publication date prior to that of the instant application,and thus are valid prior documents to incorporated by reference in theinstant application.

Having fully described at least one embodiment of the present invention,other equivalent or alternative methods of implementing machine learningsystems according to the present invention will be apparent to thoseskilled in the art. Various aspects of the invention have been describedabove by way of illustration, and the specific embodiments disclosed arenot intended to limit the invention to the particular forms disclosed.The particular implementation of the machine learning systems may varydepending upon the particular context or application. By way of example,and not limitation, the machine learning systems described in theforegoing were principally directed to point of sale systemsimplementations; however, similar techniques may instead be applied toautomated manufacturing systems and warehouse systems, whichimplementations of the present invention are contemplated as within thescope of the present invention. The invention is thus to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the following claims. It is to be further understood thatnot all of the disclosed embodiments in the foregoing specification willnecessarily satisfy or achieve each of the objects, advantages, orimprovements described in the foregoing specification.

Claim elements and steps herein may have been numbered and/or letteredsolely as an aid in readability and understanding. Any such numberingand lettering in itself is not intended to and should not be taken toindicate the ordering of elements and/or steps in the claims.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The Abstract is provided to comply with 37 C.F.R. Section 1.72(b)requiring an abstract that will allow the reader to ascertain the natureand gist of the technical disclosure. That is, the Abstract is providedmerely to introduce certain concepts and not to identify any key oressential features of the claimed subject matter. It is submitted withthe understanding that it will not be used to limit or interpret thescope or meaning of the claims.

The following claims are hereby incorporated into the detaileddescription, with each claim standing on its own as a separateembodiment.

What is claimed is:
 1. A POS system comprising: a scanner component,which can scan at least one of the following codes: Barcode, QR code,RFID or any other new code and id. a camera component, which can getimage or picture of objects. a compute component with predictionalgorithm to classify the object;
 2. A POS method comprising; Steps fora machine learning system, the method using a device with prediction andlearning capability sends the classified labels to central controller orserver; a central controller or server gathers classified labels andanalyze and learn from classified labels information; and a centralcontroller or server sends updated